Employing Relative Entropy Techniques for Assessing Modifications in Animal Behavior

: Calculating the amount of information about a random function contained in another random function has important uses in communication theory. An expression for the mutual information for continuous time random processes has been given by Gelfand and Yaglom, Chiang, and Perez by generalizing Shannon's result in a natural way. Under a condition of absolute continuity of measures the continuous time expression has the same form as Shannon's result. For two Gaussian processes Gelfand and Yaglom express the mutual information in terms of a mean square estimation error. We generalize this result to diffusion processes and express the solution in a different form which is more naturally related to a corresponding filtering problem. We also use these results to calculate some information rates.

State estimation (SE) plays a vital role in the reliable operation of modern power systems, gives situational awareness to the operators, and is employed in different functions of the Energy Management System (EMS), such as Optimal Power Flow (OPF), Contingency Analysis (CA), power market mechanism, etc. To increase SE's accuracy and protect it from compromised measurements, Bad Data Detection (BDD) algorithm is employed. However, the integration of Information and Communication Technologies (ICT) into the modern power system makes it a complicated cyber-physical system (CPS). It gives this opportunity to an adversary to find some loopholes and flaws, penetrate to CPS layer, inject false data, bypass existing BDD schemes, and consequently, result in security and stability issues. This paper employs a semi-supervised learning method to find normal data patterns and address the False Data Injection Attack (FDIA) problem. Based on this idea, the Probability Distribution Functions (PDFs) of measurement variations are derived for training and test data sets. Two distinct indices, i.e., Absolute Distance (AD) and Relative Entropy (RE), a concept in Information Theory, are utilized to find the distance between these two PDFs. In case an intruder compromises data, the related PDF changes. However, we demonstrate that AD fails to detect these changes. On the contrary, the RE index changes significantly and can properly detect FDIA. This proposed method can be used in a real-time attack detection process where the larger RE index indicates the possibility of an attack on the real-time data. To investigate the proposed methodology's effectiveness, we utilize the New York Independent System Operator (NYISO) data (Jan.-Dec. 2019) with a 5-minute resolution and map it to the IEEE 14-bus test system, and prepare an appropriate data set. After that, two different case studies (attacks on voltage magnitude ( Vm), and phase angle (θ)) with different attack parameters (i.e., 0.90, 0.95, 0.98, 1.02, 1.05, and 1.10) are defined to assess the impact of an attack on the state variables at different buses. The results show that RE index is a robust and reliable index, appropriate for real-time applications, and can detect FDIA in most of the defined case studies.

A Performance Test for a Fringe Projection Scanner in Various Ambient Light Conditions

Global Positioning System (GPS) collars are increasingly being used to study fine‐scale patterns of animal behavior. Previous studies on GPS collars have tried to determine the causes of location error without attempting to investigate whether the accuracy of fixes provides a correspondingly accurate measure of the animal's natural behavior. When comparing 2 types of GPS collar, we found a significant effect of collar weight and fit on the rate of travel of plains zebra ( Equus burchelli antiquorum ) females in the Makgadikgadi, Botswana. Although both types of collar were well within accepted norms of collar weight, the slightly heavier collars (0.6% of total body mass [TBM]) reduced rate of travel by >50% when foraging compared with the collar that was 0.4% of TBM. Collar effect was activity specific, particularly interfering with grazing behavior; the effect was less noticeable when zebras crossed larger interpatch distances. We highlight that small differences in collar weight or fit can affect specific behaviors, limiting the extrapolation of fine‐scaled GPS data. This has important implications for wildlife biologists, who hitherto have assumed that collars within accepted weight limits have little or no effect on animal movement parameters.

The ambient light is changed by the factor of nature such as a weather, season and time. The glossy object reflects an ambient light and cause blown out highlights. Size and the position of the blown out highlights are changed by the change of the ambient light. It is difficult to use a general image processing system on the condition that an ambient light changes. In the last year, we described the development of the glossy sealing component inspection system using a blue lighting. In this paper, we describe the study of better illumination color.

Fifty years ago, when Claude Shannon was developing the Mathematical Theory of Communications, for reliable data transmission, which evolved into the subject of information theory, another discipline was developing dealing with Feedback Control of Dynamical System, which evolved into a scientific subject dealing with decision, stability, and optimization. More recently, a separate discipline dealing with robustness of uncertain systems was born in response to the codification of high performance and reliability in the presence of modeling uncertainties. In principle, robustness in dynamical systems is captured through power dissipation via induced norms and dynamic games, while reliable data transmission is captured through measures of information via entropy, relative entropy, and certain laws of Large Deviations theory. The main ingredient in Large Deviations is the rate functional (or action functional in the classical mechanics terminology), often identified through the Cramer or Legendre-Fenchel Transform. On the other hand, robustness of stochastic uncertain systems is currently under development, using information theoretic as well as statistical mechanics concepts, such as, partition functions, free energy, relative entropy, and entropy rate functional. This lecture will summarize certain connections between fundamental concepts of robustness, information theory, and statistical mechanics, and possibly make future projections into the convergence of these disciplines.

Objectives: 1. To propose a framework for Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique. 2. To extract behavioral patterns from execution reports of malware in terms of its features and generates a data repository. 3. To select the most promising features using information theory based concepts. Methods/Statistical Analysis: Today, malware is a major concern of computer security experts. Variety and in- creasing number of malware affects millions of systems in the form of viruses, worms, Trojans etc. Many techniques have been proposed to analyze the malware to its class accurately. Some of analysis techniques analyzed malware based upon its structure, code flow, etc. without executing it (called static analysis), whereas other techniques (termed as dynamic analysis) focused to monitor the behavior of malware by executing it and comparing it with known malware behavior. Dynamic analysis has proved to be effective in malware detection as behavior is more difficult to mask while executing than its underlying code (static analysis). In this study, we propose a framework for Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique. The proposed framework extracts behavioral patterns from execution reports of malware in terms of its features and generates a data repository. Further, it selects the most promising features using information theory based concepts. Findings: The proposed framework detects the family of unknown malware samples after training of a classifier from malware data repository. We validated the applicability of the proposed framework by comparing with the other dynamic malware analysis technique on a real malware dataset from Virus Total. Application: The proposed framework is a Malware Classification System (MCS) to analyze malware behavior dynamically using a concept of information theory and a machine learning technique.

Both botanical composition and the presence of additional grazer species may modify the grazing efficiency and ingestive behavior of ruminants. However, at present, potential effects of interactions between sward diversity and presence of multiple animal species on the grazing behavior of cattle and sheep are poorly understood. The objective of this study was to evaluate the effect of mono- or co-grazing of sheep and cattle, as well as the influence of sward botanical composition (either diverse or grass-dominated swards) on animal behavior patterns in a fully factorial design (α=0.05). Sixteen core animals (two mono-grazing animals per species on two sward types, plus two co-grazing animals per species on two sward types) were observed simultaneously. The main behavior (grazing, walking, ruminating/resting) of core animals per paddock was recorded repeatedly in scan samplings every ten minutes from 6 to 22h on twelve observation periods distributed over the grazing seasons of 2009 and 2010. Four daylight quarters of four hours each were differentiated within one observation period. Additionally, bites per minute, bites per step and steps per minute were observed 15 times per core animal on each observation date. Behavioral observations showed that cattle decreased the time allocated to grazing in the presence of sheep (co-grazing management; P≤0.036) and ruminated/rested longer (P≤0.023). However, sward botanical composition had no effect on cattle behavior (P≥0.05). In contrast, sward botanical composition impacted sheep behavior to a greater degree than mono- or co-grazing management (P≤0.05). Both cattle and sheep had a tendency to spend more time grazing and less time ruminating/resting towards the end of the day. In cattle, longer grazing periods were followed by longer ruminating/resting times, whereas sheep varied less in their time spent grazing or ruminating throughout the day. Sheep and cattle responded differently in their grazing behavior to vegetation composition and grazing management showing a potential of the livestock to adapt to a broad range of conditions. The behavior data suggests that livestock performance might gain from co-grazing management. Cattle and sheep behavior should be considered in planning future directions for grassland management and productivity.

The fiddler crab, Uca beebei, lives in individually defended burrows, in mixed-sex colonies on intertidal mud flats. Avian predation is common, especially of crabs unable to escape into burrows. Mating pairs form in two ways. Females either mate on the surface at their burrow entrance ('surface mating') or leave their own burrow and sequentially enter and leave ('sample') courting males' burrows, before staying in one to mate underground ('burrow mating'). We tested whether perceived predation risk affects the relative frequency of these mating modes. We first observed mating under natural levels of predation during one biweekly, semi-lunar cycle. We then experimentally increased the perceived predation risk by attracting grackles (Quiscalus mexicanus) to each half of the study site in two successive biweekly cycles. In each experimental cycle, crabs were significantly less likely to mate on the side with more birds. Moreover, on the side with elevated predation risk, the number of females leaving burrows to sample was greatly reduced relative to the number of females that surface-mated. Males waved less and built fewer mud pillars, which attract females, when birds were present. We discuss several plausible proximate explanations for these results and the effect of changes in predation regime on sexual selection.

Previous work (Reynolds and Gross 1992) has demonstrated that mate choice enables female guppies, Poecilia reticulata , to obtain heritable benefits (‘good genes’) for their offspring. It is not yet known whether males also signal their functional fertility, that is, their viability as fathers, to female guppies. Our study tested the hypothesis that a male9s behaviour reveals characteristics of his ejaculate. We uncovered a strong correlation between display rate and sperm number in male guppies derived from two wild Trinidadian populations. This result indicates that information on the functional fertility of potential mates is available to females. However, our investigation also found a significant relationship between the frequency of sneaky mating and sperm number. Female guppies in wild populations, particularly those experiencing a high predation regime, are constantly subjected to sneaky mating attempts. As the males responsible for these sneaky matings have highest reserves of sperm they may have the capacity to undermine female choice. The implications of these findings for sperm competition are discussed.

The evolution of animal communication is a complex issue and one that attracts much research and debate. 'Receiver psychology' has been highlighted as a potential selective force, and we review how avian psychological processes and biases can influence the evolution and design of signals as well as the progress that has been made in testing these ideas in behavioural studies. Interestingly, although birds are a focal group for experimental psychologists and behavioural ecologists alike, the integration of theoretical ideas from psychology into studies of communication has been relatively slow. However, recent operant experiments are starting to address how birds perceive and respond to complex natural signals in an attempt to answer evolutionary problems in communication. This review outlines how a psychological approach to understanding communication is useful, and we hope that it stimulates further research addressing the role of psychological mechanisms in signal evolution.

We explore the automatic adjustment of an artificial light source intensity for the purposes of image-based feature extraction and recognition. Two histogram-based criteria are proposed to achieve this adjustment: a two-class separation measure for 2D features and a Gaussian distribution measure for 2.5D features. To this end, the light source intensity is varied within a fixed interval as a camera captures one image for each intensity variation. The image that best satisfies the criteria for feature extraction is tested on a neural-network based recognition system. The network considers information related to both 2D (contour) and 2.5D shape (local surface curvature) of different objects. Experimental tests performed during different times of the day confirm that the proposed adjustment delivers improved feature extraction, extending the recognition capabilities of the system and adding robustness against changes in ambient light.

Flowers of some Phlox (Phlox x paniculata L.) varieties undergo daily colour shifts, being blue in the early morning, turning red during the day, and returning to blue in the evening. The colour shift, which occurs only in the upper (adaxial) petal surfaces, is due to the daily changes in ambient light. In the laboratory, colour shifts could be induced by 2.5 h of ultraviolet, visible or far‐red light and recorded by reflectance spectrophotometry.There are indications that irradiations with different kinds of light cause qualitatively different colour shifts, and that thus more than one photoreceptor pigment and more than one primary light reaction may be involved. The presence of phytochrome was demonstrated in petals of white Phlox flowers by in vivo transmission spectrophotometry. It is therefore possible that colour shifts in coloured Phlox flowers are mediated by phytochrome. Possibly the movement of ions (e.g. hydrogen ions) into or out of the vacuole (where the visible pigments are located) is affected by light absorption in a pigment in the tonoplast.

Background Pupillometry has been studied as a physiological marker for quantifying cognitive load since the early 1960s. It has been established that small changes in pupillary size can provide an index of the cognitive load of a participant as he/she performs a mental task. The utility of pupillometry as a measure of expertise is less well established, although recent research in the fields of education, medicine and psychology indicates that differences in pupillary size during domain-specific tasks allows differentiation between experts and novices in appropriately designed experiments. Purpose The goal of this review is to explore the existing body of evidence for the use of pupillometry as a measure of expertise and to identify its strengths and constraints within the context of expertise research in the medical sciences. Results Pupillometry is a robust metric that allows researchers to better understand cognitive load in medical practitioners with varying levels of expertise. In medical expertise research, it has been used to study surgeons, anesthetists and emergency physicians. Its strengths include its ability to provide quantitative and objective outputs, to be measured unobtrusively with new technology and to be precisely computed as cognitive load changes over the course of completion of a task. Constraints associated with this methodology include its potential inaccuracy with changes in ambient light and pupillary accommodation as well as the need for relatively expensive equipment. Conclusion With recent technological advances, pupillometry has become a simple and robust method for quantifying physiological changes attributable to cognitive load and is increasingly being utilized in medical education. It can be used as a reliable marker of mental effort and has been shown to differentiate levels of expertise in medical practitioners.

Classification of soil aggregates: A novel approach based on deep learning